Managing the prediction of metrics in high-frequency financial markets is a challenging task. An efficient way is by monitoring the dynamics of a limit order book to identify the information edge. This paper describes the first publicly available benchmark dataset of high-frequency limit order markets for mid-price prediction. We extracted normalized data representations of time series data for five stocks from the NASDAQ Nordic stock market for a time period of ten consecutive days, leading to a dataset of ~4,000,000 time series samples in total. A day-based anchored cross-validation experimental protocol is also provided that can be used as a benchmark for comparing the performance of state-of-the-art methodologies. Performance of baseline approaches are also provided to facilitate experimental comparisons. We expect that such a large-scale dataset can serve as a testbed for devising novel solutions of expert systems for high-frequency limit order book data analysis.

中文翻译：

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使用机器学习方法对限价订单数据进行中间价预测的基准数据集

管理高频金融市场中的指标预测是一项具有挑战性的任务。一种有效的方法是通过监控限价订单簿的动态来识别信息优势。本文描述了第一个公开可用的高频限价订单市场基准数据集，用于中间价格预测。我们从纳斯达克北欧股市连续十天提取了五只股票的时间序列数据的标准化数据表示，从而产生总共约 4,000,000 个时间序列样本的数据集。还提供了基于天的锚定交叉验证实验协议，可用作比较最先进方法的性能的基准。还提供了基线方法的性能以促进实验比较。